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In recent years, significant progress has been made in the field of robotic reinforcement learning (RL), enabling methods that handle complex image observations, train in the real world, and incorporate auxiliary data, such as demonstrations and prior experience.
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Vijay R. Konda and John N. Tsitsiklis · 1999
Earlier work this paper cites.
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Anusha Nagabandi, Kurt Konolige, Sergey Levine, and Vikash Kumar · 2019
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Albert Zhan, Ruihan Zhao, Lerrel Pinto, Pieter Abbeel, and Michael Laskin · 2021
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